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For the Inventor, By the Inventor

Yorktown Heights, NY, United States of America

Payel Das

This inventor holds 23 USPTO granted patents and 25 published patent applications, primarily in Machine Learning. Top assignees: International Business Machines Corporation, Agency for Science, Technology and Research, Massachusetts Institute of Technology. Active years: 2019-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Payel Das. Retrieved from https://idiyas.com/inventor/payel-das

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 23 

% Patents Active = 65.2

Average Co-Inventor Count = 3.9

ph-index = 1

Forward Citations = 8(Granted Patents)


Company Filing History:

goldMedal23 out of 166,325 
 
International Business Machines Corporation
 patents
silverMedal1 out of 1,560 
 
Agency for Science, Technology and Research
 patents
bronzeMedal1 out of 8,578 
 
Massachusetts Institute of Technology
 patents
where one patent can have more than one assignee

Years Active: 2019-2026

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Areas of Expertise:
Causal Learning Model
Protein Design
Reinforcement Learning
Deep Surrogate Sampling
Generative Model
Molecule Optimization
Transfer Learning
Neurofeedback
Biosensor Detection
Adversarial Effects
Attribute-Based Samples
Interactive Learning
23 patents (USPTO):Explore Patents
1. 12725081 - Causal learning model (2026-09-01),
2. 12694946 - Machine learning model distillation for protein design (2026-07-28),
3. 12670340 - Reinforced generation: reinforcement learning for text and knowledge graph bi-directional generation using pretrained language models (2026-06-30),
4. 12554989 - Deep surrogate Langevin sampling for multi-objective constraint black box optimization with applications to optimal inverse design problems (2026-02-17),
5. 12450168 - Dynamic updating of content addressable associative memories for large language models (2025-10-21),
6. 12412637 - Embedding-based generative model for protein design (2025-09-09),
7. 12367397 - Query-based molecule optimization and applications to functional molecule discovery (2025-07-22),
8. 12124965 - Combinatorial black box optimization with expert advice (2024-10-22),
9. 12079730 - Transfer learning for molecular structure generation (2024-09-03),
10. 11829726 - Dual learning bridge between text and knowledge graph (2023-11-28),
11. 11568282 - Mitigating adversarial effects in machine learning systems (2023-01-31),
12. 11568267 - Inducing creativity in an artificial neural network (2023-01-31),
13. 11481626 - Generating attribute-based samples (2022-10-25),
14. 11416775 - Training robust machine learning models (2022-08-16),
15. 11174289 - Artificial intelligence designed antimicrobial peptides (2021-11-16),

Title: **Innovative Mind: The Patented Contributions of Payel Das**

Introduction

Payel Das, located in Yorktown Heights, NY, is a notable inventor with an impressive portfolio of 17 patents. Her work significantly advances the field of molecular optimization, particularly in drug discovery, showcasing her innovative spirit and technical expertise.

Latest Patents

Among her latest contributions is the patent for a query-based molecule optimization system, which includes a framework, method, and computer program product designed to optimize molecules effectively. This innovative system, known as the Query-Based Generic End-to-End Molecular Optimization (QMO), enhances drug discovery processes by decoupling representation learning from guided searches. The QMO framework integrates evaluations using chemical modeling and pre-trained machine-learning models, allowing for efficient optimizations that target drug-likeness and binding affinity to pathogens, including efforts towards combating viruses like SARS-CoV-2.

Another notable patent involves combinatorial black box optimization, where she developed a method and system for solving combinatorial optimization problems. This involves creating a multilinear polynomial surrogate model, which enhances the optimization processes and provides solutions derived from complex black-box models.

Career Highlights

Payel has made substantial contributions through her work at prominent organizations such as IBM and the Agency for Science, Technology and Research. Her experience at these institutions has propelled her to the forefront of research in molecular optimization and related technologies.

Collaborations

Throughout her career, Payel has collaborated with esteemed colleagues, including Inkit Padhi and Robert L. Bruce. These partnerships have enriched her research endeavors and contributed to groundbreaking advancements in the field of molecular sciences.

Conclusion

Payel Das stands as a beacon of innovation and creativity in the realm of molecular optimization. With her impactful patents and collaborative spirit, she continues to inspire future inventors and contribute to the advancement of drug discovery technologies. Her work not only reflects her expertise but also represents a significant leap towards improved healthcare solutions.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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